A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model
Promoting wind power clean heating technology on a large scale is important to use electric heating technology to improve the power grid regulation capability in the northern part of China at present. However, due to various factors, it is difficult to determine the relevant parameters of the electr...
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Harbin University of Science and Technology Publications
2020-10-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1865 |
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| author | WANG Hongtao ZHANG Liwei MU Gang |
| author_facet | WANG Hongtao ZHANG Liwei MU Gang |
| author_sort | WANG Hongtao |
| collection | DOAJ |
| description | Promoting wind power clean heating technology on a large scale is important to use electric heating technology to improve the power grid regulation capability in the northern part of China at present. However, due to various factors, it is difficult to determine the relevant parameters of the electric heating system after modeling. In this way, the experiment is conducted in a heating season in a district of Changchun City. Based on the measured data, the simplified first order equivalent thermal parameter (ETP) are adopted. The model approximates the working characteristics of the thermostatically controlled loads (TCL). The particle swarm optimization algorithm is used to optimize the parameters in the model, and the error is corrected by the linear regression equation. Based on this, the electric heating equipment is built. Aggregate the loads model. Finally, the aggregation load power of the electric heating equipment group was evaluated and the influencing factors through the simulation experiment. The results show that the optimized parameters R and C can accurately simulate the dynamic changes of indoor temperature in the residential area, which proves the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-29a0c9870fda4a3da71abc58c9831fdf |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2020-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-29a0c9870fda4a3da71abc58c9831fdf2025-08-20T03:40:25ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832020-10-012505233110.15938/j.jhust.2020.05.004A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load ModelWANG Hongtao0ZHANG Liwei1MU Gang2School of Electrical Engineering, Northeast Electric Power University,Jilin 132012，ChinaSchool of Electrical Engineering, Northeast Electric Power University,Jilin 132012，ChinaSchool of Electrical Engineering, Northeast Electric Power University,Jilin 132012，ChinaPromoting wind power clean heating technology on a large scale is important to use electric heating technology to improve the power grid regulation capability in the northern part of China at present. However, due to various factors, it is difficult to determine the relevant parameters of the electric heating system after modeling. In this way, the experiment is conducted in a heating season in a district of Changchun City. Based on the measured data, the simplified first order equivalent thermal parameter (ETP) are adopted. The model approximates the working characteristics of the thermostatically controlled loads (TCL). The particle swarm optimization algorithm is used to optimize the parameters in the model, and the error is corrected by the linear regression equation. Based on this, the electric heating equipment is built. Aggregate the loads model. Finally, the aggregation load power of the electric heating equipment group was evaluated and the influencing factors through the simulation experiment. The results show that the optimized parameters R and C can accurately simulate the dynamic changes of indoor temperature in the residential area, which proves the effectiveness of the proposed method.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1865thermodynamic modelthermostatically controlled loadsparameter optimizationaggregate ability |
| spellingShingle | WANG Hongtao ZHANG Liwei MU Gang A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model Journal of Harbin University of Science and Technology thermodynamic model thermostatically controlled loads parameter optimization aggregate ability |
| title | A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model |
| title_full | A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model |
| title_fullStr | A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model |
| title_full_unstemmed | A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model |
| title_short | A Parameter Optimization Method and Evaluation of Aggregation Ability of Thermostatically controlled Load Model |
| title_sort | parameter optimization method and evaluation of aggregation ability of thermostatically controlled load model |
| topic | thermodynamic model thermostatically controlled loads parameter optimization aggregate ability |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1865 |
| work_keys_str_mv | AT wanghongtao aparameteroptimizationmethodandevaluationofaggregationabilityofthermostaticallycontrolledloadmodel AT zhangliwei aparameteroptimizationmethodandevaluationofaggregationabilityofthermostaticallycontrolledloadmodel AT mugang aparameteroptimizationmethodandevaluationofaggregationabilityofthermostaticallycontrolledloadmodel AT wanghongtao parameteroptimizationmethodandevaluationofaggregationabilityofthermostaticallycontrolledloadmodel AT zhangliwei parameteroptimizationmethodandevaluationofaggregationabilityofthermostaticallycontrolledloadmodel AT mugang parameteroptimizationmethodandevaluationofaggregationabilityofthermostaticallycontrolledloadmodel |